In appearance-based methods for object detection and/or recognition, segmentation of images indicative of the objects of interest may be complicated by dynamic motion during the acquisition of a time-wise sequence of images. An exemplary application is segmentation of cardiac perfusion image data. Ischemic heart disease, the obstruction of blood flow to the heart, typically results from excess fat or plaque deposits, which may narrow the veins that supply oxygenated blood to the heart. The reduced blood supply to the heart is typically manifested as reduced blood perfusion to the myocardium (“MC”) heart muscle. Clinically, the myocardial perfusion measurements are routinely performed with single-photon emission computed tomography (“SPECT”) images, and/or with positron emission tomography (“PET”) images. Drawbacks and limitations of these existing techniques include the low spatial resolution, attenuation artifacts of SPECT and limited availability of PET.
Myocardial perfusion analysis using magnetic resonance (“MR”) images holds great promise, and also permits quantitative analysis of blood flow. In MR perfusion analysis, typically about 60 to 100 short axis 2-dimensional (“2D”) MR images of the heart are acquired after injecting contrast agent into the blood. Unfortunately, as the heart is beating, the contrast in the acquired MR images is typically rapidly changing. The contrast agent passes through the right ventricle (“RV”) to the left ventricle (“LV”), and then perfuses into the myocardium.
To perform the perfusion analysis, it is necessary to segment the myocardium in all of the MR images acquired in a perfusion scan. Segmenting the myocardium in all of the MR images is currently performed manually, and requires significant labor from skilled physicians. This is a tedious and labor-intensive job, given that there are typically 60 to 100 images in each scan. The problem is compounded by the fact that the contrast in the images is typically rapidly changing. When the contrast agent is in the LV, the blood pool brightens up and makes it easy to segment the inner wall of the myocardium, the endocardium. However, when there is no contrast agent in the LV, it is very difficult to segment the endocardium.
Segmentation of the outer boundary of the heart, the epicardium, remains difficult throughout all of the images acquired in the scan. In addition to the changing contrast, there may also be gross motion due to patient breathing and/or changes in the heart shape as it is beating. Accordingly, what is needed is an automated approach to segmentation of the myocardium, endocardium and/or epicardium in sequences of MR images.